Last modified on 2025/07/31
By Simon Coude

The dataset for the FIELDMAPS data release paper is divided in several folders, and their contents are described 
below. The data used to create the figures of the manuscript can be found under the 'FIELDMAPS_Plots/' folder, 
exceptfor the composite IR images which can instead be found under the 'FIELDMAPS_RGB/' folder.

In alphabetical order:

===

FIELDMAPS_FixingFil4/

This contains the Level 2 HAWC+ data and the scripts used to correct for the erroneous celestial positions
recorded during Flight 684 for Filament 4. See Appendix D. 

The Python scripts are run in two Jupyter notebooks. The World Coordinate System correction is measured using 
'FixingFil4_WCS.ipynb' and the FITS files are corrected with 'FixingFil4_Data.ipynb'.

===

FIELDMAPS_Hawc_to_Herschel/

This folder contains HAWC+ data of Neptune at 214 µm in total intensity and polarization modes. These data were used
to test the effective resolution of the HAWC+ data in Section 2.1.  The Python functions created for Figure 25  
are listed under 'HAWCtoHerschel.py'. The figures for eachbone are created in independent Jupyter notebooks of 
the form '****_Smoothed_Plots.ipynb'.


===

FIELDMAPS_Neptune/

This folder contains the HAWC+ data smoothed and reprojected to the resolution and pixel scale of the Herschel
data used in the manuscript. These data were used to investigate the relation between polarization fraction
and column density as well as dust temperature in Section 3.5.  

===

FIELDMAPS_Orientation/

This folder contains the FILFINDER masks (i.e., skeletons) obtained for each bone. These masks were used to 
calculate the orientation of each filament relative to Galactic North. The 'Orientations_Plots.ipynb' notebook
was used to create Figure 27. See Section 3.3.1 and Appendix I. 

===

FIELDMAPS_Planck/

This folder contains the archival Planck data and the scripts used to transform them into the IAU convention
for measuring celestial polarization. See Appendix E.2. These scripts also include the unit conversion described
in Section 2.3. 

===

FIELDMAPS_Plots/ -- Primary Folder for the Dataset 

This folder contains all the data and scripts used to produce the figures in the main body of the manuscript.
All the Python functions created for these figures are listed under 'FIELDMAPS_Plots.py'. The figures for each
bone are created in independent Jupyter notebooks of the form 'Figures_****.ipynb'. These include Figures 2 
to 13, Figures 16 and 17, Figures 21 to 24, and Figure 26. 

The data and figures for each bone is found under the folder of the same name as the bone ('Fil1/', 'Fil2/', etc.).  

Figures containing data from more than one bone are compiled underthe folder 'All/' . The circular mean and 
standard deviation of Planck polarization for each bone were calculated using 'Statistics_Planck.ipynb'. 
Figure 14 was created using 'Statistics_Distributions.ipynb'. Figure 15 was created using 'Statistics_Angles.ipynb'.
The average power-law index listed in Section 3.5 is obtained from 'Statistics_Polarization.ipynb'.

===

FIELDMAPS_Reprojection/

This folder contains the Level 4 data products obtained from the HAWC+ data reduction pipeline, and the scripts used
to reproject them to the Galactic Coordinate System. See Appendix E.1.

===

FIELDMAPS_RGB/

This folder contains all the Spitzer data used to create the composite images in Figure 1, and Figures 28 to 32.
All the Python functions created for these figures are listed under Plot_RGB.py. The figures for each
bone are created in independent Jupyter notebooks of the form '****_RGB.ipynb'. See Appendix J.

===

FIELDMAPS_Testing_Fil5/

This folder contains the data and scripts used to test the different observing modes and reduction parameters
for Filament 5. See Appendix C. 


===

The Python 3.10.8 environment used for this work directly utilized the following packages 
(and their related dependencies):
aplpy                    2.1.0
astropy                  5.3.4
lmfit                    1.1.0
matplotlib               3.6.3
numpy                    1.24.2
photutils                1.9.0
reproject                0.10.0
scipy                    1.10.0